Speech deepfake detection (DFD) has benefited from diverse acoustic and semantic speech representations, many of which encode valuable speech information and are costly to train. Prior work has shown that affective cues improve DFD, yet existing approaches either fuse emotion with other task-specific features in complex pipelines or directly fine-tune representations toward DFD objectives, risking distortion of the original speech representations that support downstream tasks such as speaker verification (SV) or automatic speech recognition (ASR). We propose a simpler approach: fine-tuning speech encoders on emotion recognition alone-without any DFD supervision, and training a lightweight support vector machine (SVM) on the frozen emotion-tuned representations for DFD. This preserves the original representation capacity for downstream tasks such as SV and ASR, while emergently improving DFD performance. Crucially, we find that emotion is uniquely effective as this bridging task: replacing it with speaker identity even degrades DFD performance, demonstrating that the benefit stems from emotion's role as a natural bridge between speech representation and DFD. Experiments on FakeOrReal and In-the-Wild show accuracy improvements of up to 6\% and 2\% with corresponding EER reductions, while analysis on ASVspoof 2019 LA reveals dataset-specific speaker bias in the real-speech subset. Code is available at supplementary materials.

翻译：语音深度伪造检测（DFD）受益于多种声学和语义语音表征，其中许多表征编码了有价值的语音信息且训练成本高昂。先前研究表明情感线索可改善DFD性能，但现有方法要么在复杂流程中将情感与其他任务特定特征融合，要么直接针对DFD目标微调表征，这可能导致支撑下游任务（如说话人验证（SV）或自动语音识别（ASR））的原始语音表征失真。我们提出一种更简单的方法：仅基于情感识别微调语音编码器——无需任何DFD监督，并在冻结的情感调优表征上训练轻量级支持向量机（SVM）用于DFD。该方法既保留了用于SV和ASR等下游任务的原始表征能力，又意外提升了DFD性能。关键在于，我们发现情感作为这种桥梁任务具有独特有效性：若将其替换为说话人身份，甚至会导致DFD性能下降，证明该收益源于情感作为语音表征与DFD之间天然桥梁的角色。在FakeOrReal和In-the-Wild数据集上的实验显示，准确率分别提升高达6%和2%，对应等错误率降低；而对ASVspoof 2019 LA数据集的分析揭示真实语音子集中存在数据集特定的说话人偏差。代码见补充材料。